Yao's minimax principle. It's not a very exciting read or a very exciting conclusion compared to some of these other papers, but it's still interesting, and the

Yao's minimax principle. It's not a very exciting read or a very exciting conclusion compared to some of these other papers, but it's still interesting, and the conclusion has been practically useful to me a small handful of times. It concerns randomized algorithms, which are algorithms that try to overcome worst case performance by randomizing their behavior, so that a malicious user can't know which input will be the worst case input this time. The principle states that the expected cost of a randomized algorithm on a single input is no better or worse than the cost of a deterministic algorithm with random input. Yao proves this is the case by constructing two zero sum games based around the algorithms' running times and then using game theory (specifically von Neumann's minimax theorem) to show that the two approaches are equivalent. It's a really neat approach!